{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c0437d31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64) (1797,)\n",
      "[[ 0.  0.  5. ...  0.  0.  0.]\n",
      " [ 0.  0.  0. ... 10.  0.  0.]\n",
      " [ 0.  0.  0. ... 16.  9.  0.]\n",
      " ...\n",
      " [ 0.  0.  1. ...  6.  0.  0.]\n",
      " [ 0.  0.  2. ... 12.  0.  0.]\n",
      " [ 0.  0. 10. ... 12.  1.  0.]] [0 1 2 ... 8 9 8]\n",
      "[[ 0.         -0.33501649 -0.04308102 ... -1.14664746 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  0.54856067 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  1.56568555  1.6951369\n",
      "  -0.19600752]\n",
      " ...\n",
      " [ 0.         -0.33501649 -0.88456568 ... -0.12952258 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -0.67419451 ...  0.8876023  -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649  1.00877481 ...  0.8876023  -0.26113572\n",
      "  -0.19600752]]\n",
      "[[ 1.91421366 -0.95450157 -3.94603482 ... -0.01797902  0.04795038\n",
      "   0.01912424]\n",
      " [ 0.58898033  0.9246358   3.92475494 ... -1.14415907  0.03774402\n",
      "   0.37167996]\n",
      " [ 1.30203906 -0.31718883  3.02333293 ...  0.48730406 -1.35695937\n",
      "  -0.10701564]\n",
      " ...\n",
      " [ 1.02259599 -0.14791087  2.46997365 ... -0.15253558  0.0509132\n",
      "   0.59550456]\n",
      " [ 1.07605522 -0.38090625 -2.45548693 ... -0.44673737 -0.46840846\n",
      "   0.16065193]\n",
      " [-1.25770233 -2.22759088  0.28362789 ...  0.43650024 -0.92270619\n",
      "  -0.06156133]]\n",
      "(1797, 34)\n",
      "直接比对预测值与真实值：\n",
      " [ True  True  True  True  True  True False  True  True  True  True  True\n",
      " False  True  True  True  True  True  True  True  True False  True  True\n",
      "  True  True  True  True  True  True  True  True False  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True False  True  True  True  True False  True\n",
      "  True  True  True  True  True False  True  True  True  True  True  True\n",
      "  True  True  True  True  True False  True  True  True  True  True  True\n",
      " False  True  True  True False  True  True  True  True  True  True False\n",
      "  True False  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True False  True False  True  True  True\n",
      "  True  True  True  True  True  True  True False  True  True False False\n",
      "  True  True False  True False  True  True  True  True  True  True  True\n",
      "  True  True False  True  True False False  True  True  True  True  True\n",
      "  True False  True  True  True  True  True  True  True False False False\n",
      "  True False  True  True  True  True  True  True  True  True  True False\n",
      "  True  True False False  True  True  True  True False  True  True  True\n",
      "  True  True  True  True False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True False  True False\n",
      "  True  True  True False  True  True  True  True  True  True  True  True\n",
      " False  True False  True  True  True False  True  True  True  True  True\n",
      " False  True  True  True  True  True  True  True  True  True  True  True\n",
      " False  True False  True  True  True  True  True  True  True  True  True\n",
      "  True  True False  True False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True False  True  True  True  True  True False\n",
      "  True  True  True False  True  True  True  True  True False  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True False False False  True False False False  True  True False\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True False  True  True  True False  True  True  True\n",
      " False  True  True  True False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True False  True  True  True\n",
      "  True  True  True False  True  True  True  True  True  True  True  True\n",
      "  True False False  True  True False  True  True False  True  True  True\n",
      "  True  True False  True  True  True  True  True  True  True False  True\n",
      " False  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True False False  True  True  True  True False  True  True  True\n",
      "  True  True  True  True  True  True  True  True False False False  True\n",
      " False  True  True  True False  True]\n",
      "准确率为：\n",
      " 0.8333333333333334\n",
      "this is a test file!\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[48  1  0  0  0  0  1  0  0  2]\n",
      " [ 0 39  0  0  0  1  0  1  0  1]\n",
      " [ 0  3 42  4  0  0  0  1  1  0]\n",
      " [ 0  0  2 26  0  0  0  1  5  6]\n",
      " [ 0  0  0  1 48  0  0  1  1  0]\n",
      " [ 1  0  0  3  0 41  0  0  4  2]\n",
      " [ 0  0  0  0  0  0 43  0  1  1]\n",
      " [ 1  0  2  1  1  0  1 34  2  2]\n",
      " [ 0  0  5  1  0  2  3  1 24  3]\n",
      " [ 1  1  0  1  0  2  0  0  0 30]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.92      0.93        52\n",
      "           1       0.89      0.93      0.91        42\n",
      "           2       0.82      0.82      0.82        51\n",
      "           3       0.70      0.65      0.68        40\n",
      "           4       0.98      0.94      0.96        51\n",
      "           5       0.89      0.80      0.85        51\n",
      "           6       0.90      0.96      0.92        45\n",
      "           7       0.87      0.77      0.82        44\n",
      "           8       0.63      0.62      0.62        39\n",
      "           9       0.64      0.86      0.73        35\n",
      "\n",
      "    accuracy                           0.83       450\n",
      "   macro avg       0.83      0.83      0.82       450\n",
      "weighted avg       0.84      0.83      0.83       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from common.utils import plot_learning_curve\n",
    "import seaborn as sn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "\n",
    "def show(image):\n",
    "    test = image.reshape((8, 8))  # 从一维变为二维，这样才能被显示\n",
    "    print(test.shape)  # 查看是否是二维数组\n",
    "    # print(test)\n",
    "    plt.imshow(test, cmap=plt.cm.gray)  # 显示灰色图像\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def standard_demo(data):\n",
    "    transfer = StandardScaler()\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def pca_demo(data):\n",
    "    transfer = PCA(n_components=0.92)\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def Tree_demo(data, label):\n",
    "    # 划分数据集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(data, label, random_state=6)\n",
    "    # 训练模型\n",
    "    estimate = DecisionTreeClassifier(criterion='entropy', splitter='best', max_depth=200)\n",
    "    estimate.fit(X_train, y_train)  # 模型构建好了\n",
    "    # 模型评估的两种方法：1：直接比对预测值与真实值；\n",
    "    y_predict = estimate.predict(X_test)\n",
    "    print(\"直接比对预测值与真实值：\\n\", y_test == y_predict)\n",
    "    # 2：计算准确率\n",
    "    score = estimate.score(X_test, y_test)\n",
    "    print(\"准确率为：\\n\", score)\n",
    "    # 绘制学习曲线!!!!\n",
    "    cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)  # 10折交叉验证\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(10, 6), dpi=144)\n",
    "    plot_learning_curve(ax, estimate, \"Learn Curve\",\n",
    "                        X_train, y_train, ylim=(0.0, 1.01), cv=cv)\n",
    "    plt.show()\n",
    "    # 混淆矩阵\n",
    "    cm = confusion_matrix(y_test, y_predict)\n",
    "    print(cm)\n",
    "    # 可视化显示混淆矩阵\n",
    "    # annot = True 显示数字 ，fmt参数不使用科学计数法进行显示\n",
    "    ax = sn.heatmap(cm, annot=True, fmt='.20g')\n",
    "    ax.set_title('confusion matrix')  # 标题\n",
    "    ax.set_xlabel('predict')  # x轴\n",
    "    ax.set_ylabel('true')  # y轴\n",
    "    plt.show()\n",
    "    # 计算精确率与召回率\n",
    "    report = classification_report(y_test, y_predict, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "                                   target_names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
    "    print(report)\n",
    "\n",
    "# 按间距中的绿色按钮以运行脚本。\n",
    "if __name__ == '__main__':\n",
    "    # 查看数据集，以图片显示\n",
    "    print(X.shape, y.shape)\n",
    "    print(X, y)\n",
    "    # show(X[1795])\n",
    "    # 数据集预处理（标准化、特征降维）\n",
    "    X_new = standard_demo(X)\n",
    "    X_new = pca_demo(X_new)\n",
    "    print(X_new.shape)  # 从64维降到了40维\n",
    "    # 机器学习建模\n",
    "    # 调参\n",
    "    # 模型评估\n",
    "    # 学习曲线\n",
    "    Tree_demo(X_new, y)  # 数据已经准备好了\n"
   ]
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   "source": []
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